bms
BMSRegressor
Bases: BaseEstimator
, RegressorMixin
Bayesian Machine Scientist.
BMS finds an optimal function to explain a dataset, given a set of variables, and a pre-defined number of parameters
This class is intended to be compatible with the Scikit-Learn Estimator API.
Examples:
>>> from autora.theorist.bms import Parallel, utils
>>> import numpy as np
>>> num_samples = 1000
>>> X = np.linspace(start=0, stop=1, num=num_samples).reshape(-1, 1)
>>> y = 15. * np.ones(num_samples)
>>> estimator = BMSRegressor()
>>> estimator = estimator.fit(X, y)
>>> estimator.predict([[15.]])
array([[15.]])
Attributes:
Name | Type | Description |
---|---|---|
pms |
Parallel
|
the bayesian (parallel) machine scientist model |
model_ |
Tree
|
represents the best-fit model |
loss_ |
float
|
represents loss associated with best-fit model |
cache_ |
List
|
record of loss_ over model fitting epochs |
Source code in autora/skl/bms.py
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|
__init__(prior_par=PRIORS, ts=TEMPERATURES, epochs=1500)
Parameters:
Name | Type | Description | Default |
---|---|---|---|
prior_par |
dict
|
a dictionary of the prior probabilities of different functions based on wikipedia data scraping |
PRIORS
|
ts |
List[float]
|
contains a list of the temperatures that the parallel ms works at |
TEMPERATURES
|
Source code in autora/skl/bms.py
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|
fit(X, y, num_param=1)
Runs the optimization for a given set of X
s and y
s.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
X |
np.ndarray
|
independent variables in an n-dimensional array |
required |
y |
np.ndarray
|
dependent variables in an n-dimensional array |
required |
num_param |
int
|
number of parameters |
1
|
Returns:
Name | Type | Description |
---|---|---|
self |
BMS
|
the fitted estimator |
Source code in autora/skl/bms.py
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|
predict(X)
Applies the fitted model to a set of independent variables X
,
to give predictions for the dependent variable y
.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
X |
np.ndarray
|
independent variables in an n-dimensional array |
required |
Returns:
Name | Type | Description |
---|---|---|
y |
np.ndarray
|
predicted dependent variable values |
Source code in autora/skl/bms.py
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present_results()
Prints out the best equation, its description length, along with a plot of how this has progressed over the course of the search tasks.
Source code in autora/skl/bms.py
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